Antiviral prescribing model

Baseline parameters

• Clinical attack rates come from the 2022/23 season burden estimates.
• Percent who are prescribed NAIs informed by VISION, VE Network, and literature review
• Care-seeking comes from FNY/ONM and VE Network
• Percent tested informed by VISION
• Test sensitivity is informed by literature for molecular rapid tests.
• Antiviral compliance is informed by ONM and RVTN

Outcomes at baseline

Extreme scenarios vs baseline

Simulate the baseline scenarios above relative to a theoretical worst-case scenario in which antivirals are not used (can be modeled by setting NAI effectiveness to 0)

## # A tibble: 1 × 3
##      mean   lower   upper
##     <dbl>   <dbl>   <dbl>
## 1 658882. 449081. 862654.

Alternative scenarios: increased individual-level parameters

Increase parameters that represent actions individuals could perform differently

• Increased % seeking care
• Increased % of care-seekers doing so within 2d
• Increased antiviral compliance

Plot new parameter distributions

Compare to worst-case scenario, assuming NAI effectiveness is at its highest value.

Alternative scenarios: increased clinical-level parameters

• Increased testing (regardless of timing)
• Increased prescribing among care-seekers with a positive test (regardless of timing)

Compare to best and worst-case scenarios, assuming NAI effectiveness is at its highest value.

Alternative scenarios: everything